Graphlab Ted Dunning  Clustering
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Graphlab Ted Dunning Clustering

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Talk on the Mahout nearest neighbor framework focussing particularly on the k-means acceleration provided by the streaming k-means implementation.

Talk on the Mahout nearest neighbor framework focussing particularly on the k-means acceleration provided by the streaming k-means implementation.

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  • 1. 1©MapR Technologies - Confidential Large-scale Single-pass k-Means Clustering at Scale Ted Dunning
  • 2. 2©MapR Technologies - Confidential Large-scale Single-pass k-Means Clustering
  • 3. 3©MapR Technologies - Confidential Large-scale k-Means Clustering
  • 4. 4©MapR Technologies - Confidential Goals  Cluster very large data sets  Facilitate large nearest neighbor search  Allow very large number of clusters  Achieve good quality – low average distance to nearest centroid on held-out data  Based on Mahout Math  Runs on Hadoop (really MapR) cluster  FAST – cluster tens of millions in minutes
  • 5. 5©MapR Technologies - Confidential Non-goals  Use map-reduce (but it is there)  Minimize the number of clusters  Support metrics other than L2
  • 6. 6©MapR Technologies - Confidential Anti-goals  Multiple passes over original data  Scale as O(k n)
  • 7. 7©MapR Technologies - Confidential Why?
  • 8. 8©MapR Technologies - Confidential K-nearest Neighbor with Super Fast k-means
  • 9. 9©MapR Technologies - Confidential What’s that?  Find the k nearest training examples  Use the average value of the target variable from them  This is easy … but hard – easy because it is so conceptually simple and you don’t have knobs to turn or models to build – hard because of the stunning amount of math – also hard because we need top 50,000 results  Initial prototype was massively too slow – 3K queries x 200K examples takes hours – needed 20M x 25M in the same time
  • 10. 10©MapR Technologies - Confidential How We Did It  2 week hackathon with 6 developers from customer bank  Agile-ish development  To avoid IP issues – all code is Apache Licensed (no ownership question) – all data is synthetic (no question of private data) – all development done on individual machines, hosting on Github – open is easier than closed (in this case)  Goal is new open technology to facilitate new closed solutions  Ambitious goal of ~ 1,000,000 x speedup
  • 11. 11©MapR Technologies - Confidential How We Did It  2 week hackathon with 6 developers from customer bank  Agile-ish development  To avoid IP issues – all code is Apache Licensed (no ownership question) – all data is synthetic (no question of private data) – all development done on individual machines, hosting on Github – open is easier than closed (in this case)  Goal is new open technology to facilitate new closed solutions  Ambitious goal of ~ 1,000,000 x speedup – well, really only 100-1000x after basic hygiene
  • 12. 12©MapR Technologies - Confidential What We Did  Mechanism for extending Mahout Vectors – DelegatingVector, WeightedVector, Centroid  Shared memory matrix – FileBasedMatrix uses mmap to share very large dense matrices  Searcher interface – ProjectionSearch, KmeansSearch, LshSearch, Brute  Super-fast clustering – Kmeans, StreamingKmeans
  • 13. 13©MapR Technologies - Confidential Projection Search java.lang.TreeSet!
  • 14. 14©MapR Technologies - Confidential How Many Projections?
  • 15. 15©MapR Technologies - Confidential K-means Search  Simple Idea – pre-cluster the data – to find the nearest points, search the nearest clusters  Recursive application – to search a cluster, use a Searcher!
  • 16. 16©MapR Technologies - Confidential
  • 17. 17©MapR Technologies - Confidential x
  • 18. 18©MapR Technologies - Confidential
  • 19. 19©MapR Technologies - Confidential
  • 20. 20©MapR Technologies - Confidential x
  • 21. 21©MapR Technologies - Confidential But This Requires k-means!  Need a new k-means algorithm to get speed – Hadoop is very slow at iterative map-reduce – Maybe Pregel clones like Giraph would be better – Or maybe not  Streaming k-means is – One pass (through the original data) – Very fast (20 us per data point with threads) – Very parallelizable
  • 22. 22©MapR Technologies - Confidential Basic Method  Use a single pass of k-means with very many clusters – output is a bad-ish clustering but a good surrogate  Use weighted centroids from step 1 to do in-memory clustering – output is a good clustering with fewer clusters
  • 23. 23©MapR Technologies - Confidential Algorithmic Details Foreach data point xn compute distance to nearest centroid, ∂ sample u, if u > ∂/ß add to nearest centroid else create new centroid if number of centroids > 10 log n recursively cluster centroids set ß = 1.5 ß if number of centroids did not decrease
  • 24. 24©MapR Technologies - Confidential How It Works  Result is large set of centroids – these provide approximation of original distribution – we can cluster centroids to get a close approximation of clustering original – or we can just use the result directly
  • 25. 25©MapR Technologies - Confidential Parallel Speedup? 1 2 3 4 5 20 10 100 20 30 40 50 200 Threads Timeperpoint(μs) 2 3 4 5 6 8 10 12 14 16 Threaded version Non- threaded Perfect Scaling ✓
  • 26. 26©MapR Technologies - Confidential Warning, Recursive Descent  Inner loop requires finding nearest centroid  With lots of centroids, this is slow  But wait, we have classes to accelerate that!
  • 27. 27©MapR Technologies - Confidential Warning, Recursive Descent  Inner loop requires finding nearest centroid  With lots of centroids, this is slow  But wait, we have classes to accelerate that! (Let’s not use k-means searcher, though)
  • 28. 28©MapR Technologies - Confidential Warning, Recursive Descent  Inner loop requires finding nearest centroid  With lots of centroids, this is slow  But wait, we have classes to accelerate that! (Let’s not use k-means searcher, though)  Empirically, projection search beats 64 bit LSH by a bit
  • 29. 29©MapR Technologies - Confidential Moving to Scale  Map-reduce implementation nearly trivial  Map: rough-cluster input data, output ß, weighted centroids  Reduce: – single reducer gets all centroids – if too many centroids, merge using recursive clustering – optionally do final clustering in-memory  Combiner possible, but essentially never important
  • 30. 30©MapR Technologies - Confidential  Contact: – tdunning@maprtech.com – @ted_dunning  Slides and such: – http://info.mapr.com/ted-mlconf Hash tags: #mlconf #mahout #mapr